A battery appearance defect detection method based on dimension reduction and point cloud data matching

Abstract

The invention discloses a battery appearance defect detection method based on dimension reduction and point cloud data matching, belonging to the technical field of machine vision detection, which obtains three-dimensional point cloud data of a battery to be detected and reduces the dimension of the point cloud data. Obtaining a defect area of the battery to be detected, and extracting point clouddata of the defect area; Extracting the point cloud data of the same area of the standard battery; Sampling two pieces of point cloud data and matching searching; acquiring The optimized correspondence of feature points and calculating roughly rigid transformation relation to realize rough registration. Performing Constraint detection on the rough registration results, and verifying correctness.Optimizing the rigid transformation relationship between the point cloud data to achieve automatic and accurate registration to determine whether the battery appearance is qualified or not. Changing A3D imagen into a 2D image by a dimension reduction algorithm, and acquiring a point cloud data of a defect area by using a plane defect detection technology in that 2D image so that the point cloud data is matched, the detection range is narrowed, the running time is reduce, and the accuracy is improved.

Description

technical field [0001] The invention relates to a battery appearance defect detection method, in particular to a battery appearance defect detection method based on dimensionality reduction and point cloud data matching, which belongs to the technical field of machine vision detection. Background technique [0002] With the increase in the production volume of modern factories and the miniaturization of components and parts, many people choose visual inspection systems to inspect mass-produced industrial parts, such as: electronic connectors, auto parts, SMT circuit boards and screws And other products, by comparing the image of the detected object with the standard product or the inspection program compiled by computer-aided design, the flaws or defects are detected. At present, two-dimensional image inspection is more common, but two-dimensional image inspection is very easy to be affected by the light source. Similarly, the surface detection of mobile phone batteries is v...

AI Technical Summary

Problems solved by technology

[0002] With the increase in the production volume of modern factories and the miniaturization of components and parts, many people choose visual inspection systems to inspect mass-produced industrial parts, such as: electronic connectors, auto parts, SMT circuit boards and screws And other products, by comparing the image of the detected object with the standard product or the inspection program compiled by computer-aided design, the flaws or defects are detected. At present, two-dimensional image inspection is more common, but two-dimensional image inspection is very easy to be affected by the light source. Similarly, the surface detection of mobile phone batteries is very easily affected by factors such as light sources, and sometimes cannot effectively reflect defect information such as pits or bumps on the battery surface, because these defects involve depth information, and two-dimensional image processing cannot detect defects. Depth information makes it difficult to locate and extract these defect features, so it cannot be well detected whether the battery meets the requirements

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